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Business Intelligence part

A traditional Data Science Project – Components

Those terms are more or less similar, all targeting to study data. Business Analytics is used in comparison with Business Intelligence, and is a common naming in commercial projects & tender, where Data Science is more a name used in university & research Center.

Machine Learning is an extension of Data Science, we see it as a learning model that could improve itself by analyzing its results & using some adaptive parameters to reduce the difference between the “forecast” and the “real”.

For example, we built a model to forecast water consumption in convenience stores, but our model is able to run on itself to compare its prediction with the reality, and then is able to adjust its forecast algorithm to better take into account those differences between what the predictive model provided and the daily consumption in stores.

By the way, funny to notice in Business Intelligence, we had always this same kind of naming discussion, for example to explain the difference between Report and Dashboard

YES. Vanilla Air can run any R program and deploy any R packages, this is one of the strength of the platform. R is the de facto language to build and deploy Analytics & Predictive Models, with hundreds of thousands of developer worldwide, making easy the adoption of Vanilla for anybody who want to deploy his packages at enterprise level.

Using Vanilla Air , you immediately take advantage of a cluster ready platform, making it possible to
run your R packages in a cluster of R services, a platform ready to scale with your growing requests
for complex data analysis

Does Vanilla Air contains preprocessing features?

YES. Vanilla Air can run an impressive number of pre-processing methods on any dataset, like cluster, filter, classification, new column calculation (for class allocation), correlation between columns, making it easy for user to discover their dataset, manage quality

How Vanilla Air can help me to analyze my own data?

Vanilla Air comes with a Dataset interface to connect with any kind of external data, data available in any kind of database, csv/text files located on disk or Hdfs, and also Vanilla Hub dataset. We make it easy to manipulate the data before starting to build your analysis with R language (filter, data visualization, correlation …), by turning any Dataset into a R dataframe . Using Vanilla Air, no more headache to integrate text file or database dataset into a R program.

Can you tell me more about cubes insideVanilla Air?

Vanilla Air provides an interface to manipulate a dataset and create a virtual cube to view the dataset as a cube, with dimensions and measures, using Vanilla Analysis technologies. Cube analysis is an important part of data pre-analysis tasks, as it allows developer to create dimension that bundle together to explain key measures.

What kind of process can be scheduled?

Virtually any program can be scheduled, as Vanilla Air provides a Workflow interface to design and run complex process that can acquire data, run any R program, and save the result set or the program output in any database or text file. Any Workflow can be scheduled and even called from an external program using Web Services call or command line interface.

Using Vanilla Hub, you can virtually connect to any kind of database and schedule retrieval process

How Vanilla Hub runs together with Vanilla ETL?

Vanilla Hub can send any dataset to Vanilla ETL, in order for Vanilla ETL to run complex transformation and takes advantage of Vanilla Architect, our Master Data Management platform, to apply conversion rules on data. Vanilla Hub is more an EAI/ETL platform, focusing on data acquisition and data storage on Hadoop, it don’t overlap with Vanilla ETL features.

When it comes to acquire complex data in real time, we do believe there is a need to separate data acquisition process, as part of Vanilla Hub set of features, and data transformation process, which is taking in charge by ETL platform, such as Vanilla ETL.

Vanilla Hub, Smart Data real time data integration module, is able to deploy custom Plugin to access and collect any kind of data. Vanilla ETL provides bullet-proof infrastructure to run complex transformation and load data into any Big Data instance.

This is a major difference with other Data Science platform, which don’t provide complex transformation infrastructure, but only limited transformation process. We took again advantage of our experience with Vanilla Bi and Vanilla ETL platforms to make the difference in terms of ready-to-deploy infrastructure.